Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations7043
Missing cells5174
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory1.4 KiB

Variable types

Text4
Categorical16
Numeric7
Boolean6

Alerts

Count has constant value "1" Constant
Country has constant value "United States" Constant
State has constant value "California" Constant
Churn Label is highly overall correlated with Churn Reason and 2 other fieldsHigh correlation
Churn Reason is highly overall correlated with Churn Label and 1 other fieldsHigh correlation
Churn Score is highly overall correlated with Churn Label and 1 other fieldsHigh correlation
Churn Value is highly overall correlated with Churn Label and 2 other fieldsHigh correlation
Contract is highly overall correlated with Tenure MonthsHigh correlation
Device Protection is highly overall correlated with Internet Service and 6 other fieldsHigh correlation
Internet Service is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Latitude is highly overall correlated with Longitude and 1 other fieldsHigh correlation
Longitude is highly overall correlated with Latitude and 1 other fieldsHigh correlation
Monthly Charges is highly overall correlated with Device Protection and 8 other fieldsHigh correlation
Multiple Lines is highly overall correlated with Monthly Charges and 1 other fieldsHigh correlation
Online Backup is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Online Security is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Phone Service is highly overall correlated with Monthly Charges and 1 other fieldsHigh correlation
Streaming Movies is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Streaming TV is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Tech Support is highly overall correlated with Device Protection and 6 other fieldsHigh correlation
Tenure Months is highly overall correlated with ContractHigh correlation
Zip Code is highly overall correlated with Latitude and 1 other fieldsHigh correlation
Phone Service is highly imbalanced (54.1%) Imbalance
Churn Reason has 5174 (73.5%) missing values Missing
CustomerID has unique values Unique

Reproduction

Analysis started2025-04-20 13:26:20.383952
Analysis finished2025-04-20 13:26:39.535067
Duration19.15 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CustomerID
Text

Unique 

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size405.9 KiB
2025-04-20T18:56:40.566388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row3668-QPYBK
2nd row9237-HQITU
3rd row9305-CDSKC
4th row7892-POOKP
5th row0280-XJGEX
ValueCountFrequency (%)
3668-qpybk 1
 
< 0.1%
8168-uqwwf 1
 
< 0.1%
7892-pookp 1
 
< 0.1%
0280-xjgex 1
 
< 0.1%
4190-mfluw 1
 
< 0.1%
8779-qrdmv 1
 
< 0.1%
1066-jksgk 1
 
< 0.1%
6467-chfzw 1
 
< 0.1%
8665-utdhz 1
 
< 0.1%
8773-hhuoz 1
 
< 0.1%
Other values (7033) 7033
99.9%
2025-04-20T18:56:42.032724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 7043
 
10.0%
2 2901
 
4.1%
9 2881
 
4.1%
6 2870
 
4.1%
7 2836
 
4.0%
0 2831
 
4.0%
8 2812
 
4.0%
5 2810
 
4.0%
3 2791
 
4.0%
1 2726
 
3.9%
Other values (27) 37929
53.9%

Count
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size344.0 KiB
1
7043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7043
100.0%

Length

2025-04-20T18:56:42.453223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:42.753168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7043
100.0%

Most occurring characters

ValueCountFrequency (%)
1 7043
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7043
100.0%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size426.6 KiB
United States
7043 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters91559
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 7043
100.0%

Length

2025-04-20T18:56:43.048719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:43.357770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
united 7043
50.0%
states 7043
50.0%

Most occurring characters

ValueCountFrequency (%)
t 21129
23.1%
e 14086
15.4%
U 7043
 
7.7%
n 7043
 
7.7%
i 7043
 
7.7%
d 7043
 
7.7%
7043
 
7.7%
S 7043
 
7.7%
a 7043
 
7.7%
s 7043
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 21129
23.1%
e 14086
15.4%
U 7043
 
7.7%
n 7043
 
7.7%
i 7043
 
7.7%
d 7043
 
7.7%
7043
 
7.7%
S 7043
 
7.7%
a 7043
 
7.7%
s 7043
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 21129
23.1%
e 14086
15.4%
U 7043
 
7.7%
n 7043
 
7.7%
i 7043
 
7.7%
d 7043
 
7.7%
7043
 
7.7%
S 7043
 
7.7%
a 7043
 
7.7%
s 7043
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 21129
23.1%
e 14086
15.4%
U 7043
 
7.7%
n 7043
 
7.7%
i 7043
 
7.7%
d 7043
 
7.7%
7043
 
7.7%
S 7043
 
7.7%
a 7043
 
7.7%
s 7043
 
7.7%

State
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size405.9 KiB
California
7043 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia

Common Values

ValueCountFrequency (%)
California 7043
100.0%

Length

2025-04-20T18:56:43.678110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:43.976249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
california 7043
100.0%

Most occurring characters

ValueCountFrequency (%)
a 14086
20.0%
i 14086
20.0%
C 7043
10.0%
l 7043
10.0%
f 7043
10.0%
o 7043
10.0%
r 7043
10.0%
n 7043
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14086
20.0%
i 14086
20.0%
C 7043
10.0%
l 7043
10.0%
f 7043
10.0%
o 7043
10.0%
r 7043
10.0%
n 7043
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14086
20.0%
i 14086
20.0%
C 7043
10.0%
l 7043
10.0%
f 7043
10.0%
o 7043
10.0%
r 7043
10.0%
n 7043
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14086
20.0%
i 14086
20.0%
C 7043
10.0%
l 7043
10.0%
f 7043
10.0%
o 7043
10.0%
r 7043
10.0%
n 7043
10.0%

City
Text

Distinct1129
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size400.6 KiB
2025-04-20T18:56:44.966614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.2232003
Min length3

Characters and Unicode

Total characters64959
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowLos Angeles
3rd rowLos Angeles
4th rowLos Angeles
5th rowLos Angeles
ValueCountFrequency (%)
san 584
 
5.7%
los 350
 
3.4%
angeles 305
 
3.0%
valley 183
 
1.8%
santa 182
 
1.8%
beach 172
 
1.7%
city 165
 
1.6%
diego 150
 
1.5%
sacramento 116
 
1.1%
jose 112
 
1.1%
Other values (1131) 8000
77.5%
2025-04-20T18:56:46.569664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6951
 
10.7%
e 6152
 
9.5%
n 5117
 
7.9%
o 4943
 
7.6%
l 4007
 
6.2%
r 3649
 
5.6%
i 3371
 
5.2%
3276
 
5.0%
s 2924
 
4.5%
t 2708
 
4.2%
Other values (42) 21861
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6951
 
10.7%
e 6152
 
9.5%
n 5117
 
7.9%
o 4943
 
7.6%
l 4007
 
6.2%
r 3649
 
5.6%
i 3371
 
5.2%
3276
 
5.0%
s 2924
 
4.5%
t 2708
 
4.2%
Other values (42) 21861
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6951
 
10.7%
e 6152
 
9.5%
n 5117
 
7.9%
o 4943
 
7.6%
l 4007
 
6.2%
r 3649
 
5.6%
i 3371
 
5.2%
3276
 
5.0%
s 2924
 
4.5%
t 2708
 
4.2%
Other values (42) 21861
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6951
 
10.7%
e 6152
 
9.5%
n 5117
 
7.9%
o 4943
 
7.6%
l 4007
 
6.2%
r 3649
 
5.6%
i 3371
 
5.2%
3276
 
5.0%
s 2924
 
4.5%
t 2708
 
4.2%
Other values (42) 21861
33.7%

Zip Code
Real number (ℝ)

High correlation 

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93521.965
Minimum90001
Maximum96161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:56:47.061149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90232
Q192102
median93552
Q395351
95-th percentile96031
Maximum96161
Range6160
Interquartile range (IQR)3249

Descriptive statistics

Standard deviation1865.7946
Coefficient of variation (CV)0.019950335
Kurtosis-1.1540426
Mean93521.965
Median Absolute Deviation (MAD)1641
Skewness-0.25146349
Sum6.586752 × 108
Variance3481189.3
MonotonicityNot monotonic
2025-04-20T18:56:47.480841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90003 5
 
0.1%
91436 5
 
0.1%
91916 5
 
0.1%
91913 5
 
0.1%
91911 5
 
0.1%
91786 5
 
0.1%
91784 5
 
0.1%
91780 5
 
0.1%
91765 5
 
0.1%
91759 5
 
0.1%
Other values (1642) 6993
99.3%
ValueCountFrequency (%)
90001 5
0.1%
90002 5
0.1%
90003 5
0.1%
90004 5
0.1%
90005 5
0.1%
90006 5
0.1%
90007 5
0.1%
90008 5
0.1%
90010 5
0.1%
90011 5
0.1%
ValueCountFrequency (%)
96161 4
0.1%
96150 4
0.1%
96148 4
0.1%
96146 4
0.1%
96145 4
0.1%
96143 4
0.1%
96142 4
0.1%
96141 4
0.1%
96140 4
0.1%
96137 4
0.1%
Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size486.9 KiB
2025-04-20T18:56:48.833542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length21.777084
Min length18

Characters and Unicode

Total characters153376
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33.964131, -118.272783
2nd row34.059281, -118.30742
3rd row34.048013, -118.293953
4th row34.062125, -118.315709
5th row34.039224, -118.266293
ValueCountFrequency (%)
121.994813 8
 
0.1%
33.964131 5
 
< 0.1%
118.062611 5
 
< 0.1%
33.940884 5
 
< 0.1%
118.128628 5
 
< 0.1%
33.771612 5
 
< 0.1%
118.143866 5
 
< 0.1%
33.833699 5
 
< 0.1%
118.314387 5
 
< 0.1%
33.807882 5
 
< 0.1%
Other values (3293) 14033
99.6%
2025-04-20T18:56:50.430423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20397
13.3%
3 16446
10.7%
. 14086
9.2%
2 13706
8.9%
8 11002
 
7.2%
7 10782
 
7.0%
4 10512
 
6.9%
9 9545
 
6.2%
6 8950
 
5.8%
5 8675
 
5.7%
Other values (4) 29275
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20397
13.3%
3 16446
10.7%
. 14086
9.2%
2 13706
8.9%
8 11002
 
7.2%
7 10782
 
7.0%
4 10512
 
6.9%
9 9545
 
6.2%
6 8950
 
5.8%
5 8675
 
5.7%
Other values (4) 29275
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20397
13.3%
3 16446
10.7%
. 14086
9.2%
2 13706
8.9%
8 11002
 
7.2%
7 10782
 
7.0%
4 10512
 
6.9%
9 9545
 
6.2%
6 8950
 
5.8%
5 8675
 
5.7%
Other values (4) 29275
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20397
13.3%
3 16446
10.7%
. 14086
9.2%
2 13706
8.9%
8 11002
 
7.2%
7 10782
 
7.0%
4 10512
 
6.9%
9 9545
 
6.2%
6 8950
 
5.8%
5 8675
 
5.7%
Other values (4) 29275
19.1%

Latitude
Real number (ℝ)

High correlation 

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.282441
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:56:50.944124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.980678
Q134.030915
median36.391777
Q338.224869
95-th percentile40.557314
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.193954

Descriptive statistics

Standard deviation2.4557226
Coefficient of variation (CV)0.067683499
Kurtosis-1.1356071
Mean36.282441
Median Absolute Deviation (MAD)2.263493
Skewness0.30386729
Sum255537.23
Variance6.0305734
MonotonicityNot monotonic
2025-04-20T18:56:51.395097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.964131 5
 
0.1%
34.152875 5
 
0.1%
32.912664 5
 
0.1%
32.64164 5
 
0.1%
32.607964 5
 
0.1%
34.105493 5
 
0.1%
34.141146 5
 
0.1%
34.101608 5
 
0.1%
33.992416 5
 
0.1%
34.231318 5
 
0.1%
Other values (1642) 6993
99.3%
ValueCountFrequency (%)
32.555828 5
0.1%
32.578103 5
0.1%
32.579134 5
0.1%
32.587557 5
0.1%
32.605012 5
0.1%
32.607964 5
0.1%
32.619465 5
0.1%
32.622999 5
0.1%
32.636792 5
0.1%
32.64164 5
0.1%
ValueCountFrequency (%)
41.962127 4
0.1%
41.950683 4
0.1%
41.949216 4
0.1%
41.932207 4
0.1%
41.924174 4
0.1%
41.867908 4
0.1%
41.831901 4
0.1%
41.816595 4
0.1%
41.813521 4
0.1%
41.769709 4
0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct1651
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.79888
Minimum-124.30137
Maximum-114.1929
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.2 KiB
2025-04-20T18:56:51.794117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-124.30137
5-th percentile-122.99873
Q1-121.81541
median-119.73089
Q3-118.04324
95-th percentile-116.76058
Maximum-114.1929
Range10.108471
Interquartile range (IQR)3.772175

Descriptive statistics

Standard deviation2.1578891
Coefficient of variation (CV)-0.018012598
Kurtosis-1.1360498
Mean-119.79888
Median Absolute Deviation (MAD)1.824786
Skewness-0.040792383
Sum-843743.51
Variance4.6564853
MonotonicityNot monotonic
2025-04-20T18:56:52.298409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-121.994813 8
 
0.1%
-118.272783 5
 
0.1%
-117.662032 5
 
0.1%
-116.635387 5
 
0.1%
-116.985026 5
 
0.1%
-117.059459 5
 
0.1%
-117.660934 5
 
0.1%
-117.655583 5
 
0.1%
-118.055848 5
 
0.1%
-117.807874 5
 
0.1%
Other values (1641) 6990
99.2%
ValueCountFrequency (%)
-124.301372 4
0.1%
-124.240051 4
0.1%
-124.217378 4
0.1%
-124.210902 4
0.1%
-124.189977 4
0.1%
-124.163234 4
0.1%
-124.15428 4
0.1%
-124.121504 4
0.1%
-124.108897 4
0.1%
-124.098739 4
0.1%
ValueCountFrequency (%)
-114.192901 5
0.1%
-114.36514 5
0.1%
-114.702256 4
0.1%
-114.71612 5
0.1%
-114.717964 5
0.1%
-114.758334 5
0.1%
-114.850784 5
0.1%
-115.152865 5
0.1%
-115.191857 5
0.1%
-115.257009 5
0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.5 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3555
50.5%
Female 3488
49.5%

Length

2025-04-20T18:56:52.823260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:53.264020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 3555
50.5%
female 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5901 
True
1142 
ValueCountFrequency (%)
False 5901
83.8%
True 1142
 
16.2%
2025-04-20T18:56:53.632662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Partner
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False 3641
51.7%
True 3402
48.3%
2025-04-20T18:56:53.968154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Dependents
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5416 
True
1627 
ValueCountFrequency (%)
False 5416
76.9%
True 1627
 
23.1%
2025-04-20T18:56:54.229872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Tenure Months
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.371149
Minimum0
Maximum72
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:56:54.562125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.559481
Coefficient of variation (CV)0.75868426
Kurtosis-1.3873716
Mean32.371149
Median Absolute Deviation (MAD)22
Skewness0.23953975
Sum227990
Variance603.16811
MonotonicityNot monotonic
2025-04-20T18:56:54.901143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
8 123
 
1.7%
70 119
 
1.7%
Other values (63) 4778
67.8%
ValueCountFrequency (%)
0 11
 
0.2%
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

Phone Service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True 6361
90.3%
False 682
 
9.7%
2025-04-20T18:56:55.183098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Multiple Lines
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size363.1 KiB
No
3390 
Yes
2971 
No phone service
682 

Length

Max length16
Median length3
Mean length3.7775096
Min length2

Characters and Unicode

Total characters26605
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 3390
48.1%
Yes 2971
42.2%
No phone service 682
 
9.7%

Length

2025-04-20T18:56:55.467592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:55.743103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4072
48.4%
yes 2971
35.3%
phone 682
 
8.1%
service 682
 
8.1%

Most occurring characters

ValueCountFrequency (%)
e 5017
18.9%
o 4754
17.9%
N 4072
15.3%
s 3653
13.7%
Y 2971
11.2%
1364
 
5.1%
p 682
 
2.6%
h 682
 
2.6%
n 682
 
2.6%
r 682
 
2.6%
Other values (3) 2046
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5017
18.9%
o 4754
17.9%
N 4072
15.3%
s 3653
13.7%
Y 2971
11.2%
1364
 
5.1%
p 682
 
2.6%
h 682
 
2.6%
n 682
 
2.6%
r 682
 
2.6%
Other values (3) 2046
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5017
18.9%
o 4754
17.9%
N 4072
15.3%
s 3653
13.7%
Y 2971
11.2%
1364
 
5.1%
p 682
 
2.6%
h 682
 
2.6%
n 682
 
2.6%
r 682
 
2.6%
Other values (3) 2046
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5017
18.9%
o 4754
17.9%
N 4072
15.3%
s 3653
13.7%
Y 2971
11.2%
1364
 
5.1%
p 682
 
2.6%
h 682
 
2.6%
n 682
 
2.6%
r 682
 
2.6%
Other values (3) 2046
7.7%

Internet Service
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.5 KiB
Fiber optic
3096 
DSL
2421 
No
1526 

Length

Max length11
Median length3
Mean length6.3000142
Min length2

Characters and Unicode

Total characters44371
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSL
2nd rowFiber optic
3rd rowFiber optic
4th rowFiber optic
5th rowFiber optic

Common Values

ValueCountFrequency (%)
Fiber optic 3096
44.0%
DSL 2421
34.4%
No 1526
21.7%

Length

2025-04-20T18:56:56.061673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:56.349115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fiber 3096
30.5%
optic 3096
30.5%
dsl 2421
23.9%
no 1526
15.1%

Most occurring characters

ValueCountFrequency (%)
i 6192
14.0%
o 4622
10.4%
F 3096
 
7.0%
b 3096
 
7.0%
e 3096
 
7.0%
r 3096
 
7.0%
3096
 
7.0%
p 3096
 
7.0%
t 3096
 
7.0%
c 3096
 
7.0%
Other values (4) 8789
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6192
14.0%
o 4622
10.4%
F 3096
 
7.0%
b 3096
 
7.0%
e 3096
 
7.0%
r 3096
 
7.0%
3096
 
7.0%
p 3096
 
7.0%
t 3096
 
7.0%
c 3096
 
7.0%
Other values (4) 8789
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6192
14.0%
o 4622
10.4%
F 3096
 
7.0%
b 3096
 
7.0%
e 3096
 
7.0%
r 3096
 
7.0%
3096
 
7.0%
p 3096
 
7.0%
t 3096
 
7.0%
c 3096
 
7.0%
Other values (4) 8789
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6192
14.0%
o 4622
10.4%
F 3096
 
7.0%
b 3096
 
7.0%
e 3096
 
7.0%
r 3096
 
7.0%
3096
 
7.0%
p 3096
 
7.0%
t 3096
 
7.0%
c 3096
 
7.0%
Other values (4) 8789
19.8%

Online Security
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.2 KiB
No
3498 
Yes
2019 
No internet service
1526 

Length

Max length19
Median length3
Mean length5.9700412
Min length2

Characters and Unicode

Total characters42047
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 3498
49.7%
Yes 2019
28.7%
No internet service 1526
21.7%

Length

2025-04-20T18:56:56.671090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:57.043492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 5024
49.8%
yes 2019
20.0%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8123
19.3%
N 5024
11.9%
o 5024
11.9%
s 3545
8.4%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2019
 
4.8%
Other values (2) 3052
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8123
19.3%
N 5024
11.9%
o 5024
11.9%
s 3545
8.4%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2019
 
4.8%
Other values (2) 3052
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8123
19.3%
N 5024
11.9%
o 5024
11.9%
s 3545
8.4%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2019
 
4.8%
Other values (2) 3052
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8123
19.3%
N 5024
11.9%
o 5024
11.9%
s 3545
8.4%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2019
 
4.8%
Other values (2) 3052
 
7.3%

Online Backup
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.6 KiB
No
3088 
Yes
2429 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.028255
Min length2

Characters and Unicode

Total characters42457
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 3088
43.8%
Yes 2429
34.5%
No internet service 1526
21.7%

Length

2025-04-20T18:56:57.339045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:57.582601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4614
45.7%
yes 2429
24.1%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8533
20.1%
N 4614
10.9%
o 4614
10.9%
s 3955
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2429
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8533
20.1%
N 4614
10.9%
o 4614
10.9%
s 3955
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2429
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8533
20.1%
N 4614
10.9%
o 4614
10.9%
s 3955
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2429
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8533
20.1%
N 4614
10.9%
o 4614
10.9%
s 3955
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2429
 
5.7%
Other values (2) 3052
 
7.2%

Device Protection
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.6 KiB
No
3095 
Yes
2422 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.0272611
Min length2

Characters and Unicode

Total characters42450
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 3095
43.9%
Yes 2422
34.4%
No internet service 1526
21.7%

Length

2025-04-20T18:56:57.981553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:58.310932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4621
45.8%
yes 2422
24.0%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8526
20.1%
N 4621
10.9%
o 4621
10.9%
s 3948
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2422
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8526
20.1%
N 4621
10.9%
o 4621
10.9%
s 3948
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2422
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8526
20.1%
N 4621
10.9%
o 4621
10.9%
s 3948
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2422
 
5.7%
Other values (2) 3052
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8526
20.1%
N 4621
10.9%
o 4621
10.9%
s 3948
9.3%
3052
 
7.2%
i 3052
 
7.2%
n 3052
 
7.2%
t 3052
 
7.2%
r 3052
 
7.2%
Y 2422
 
5.7%
Other values (2) 3052
 
7.2%

Tech Support
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.2 KiB
No
3473 
Yes
2044 
No internet service
1526 

Length

Max length19
Median length3
Mean length5.9735908
Min length2

Characters and Unicode

Total characters42072
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 3473
49.3%
Yes 2044
29.0%
No internet service 1526
21.7%

Length

2025-04-20T18:56:58.609186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:58.878268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4999
49.5%
yes 2044
20.2%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8148
19.4%
N 4999
11.9%
o 4999
11.9%
s 3570
8.5%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2044
 
4.9%
Other values (2) 3052
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8148
19.4%
N 4999
11.9%
o 4999
11.9%
s 3570
8.5%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2044
 
4.9%
Other values (2) 3052
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8148
19.4%
N 4999
11.9%
o 4999
11.9%
s 3570
8.5%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2044
 
4.9%
Other values (2) 3052
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8148
19.4%
N 4999
11.9%
o 4999
11.9%
s 3570
8.5%
3052
 
7.3%
i 3052
 
7.3%
n 3052
 
7.3%
t 3052
 
7.3%
r 3052
 
7.3%
Y 2044
 
4.9%
Other values (2) 3052
 
7.3%

Streaming TV
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
No
2810 
Yes
2707 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.0677268
Min length2

Characters and Unicode

Total characters42735
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 2810
39.9%
Yes 2707
38.4%
No internet service 1526
21.7%

Length

2025-04-20T18:56:59.204385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:56:59.488133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4336
43.0%
yes 2707
26.8%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8811
20.6%
N 4336
10.1%
o 4336
10.1%
s 4233
9.9%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2707
 
6.3%
Other values (2) 3052
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8811
20.6%
N 4336
10.1%
o 4336
10.1%
s 4233
9.9%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2707
 
6.3%
Other values (2) 3052
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8811
20.6%
N 4336
10.1%
o 4336
10.1%
s 4233
9.9%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2707
 
6.3%
Other values (2) 3052
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8811
20.6%
N 4336
10.1%
o 4336
10.1%
s 4233
9.9%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2707
 
6.3%
Other values (2) 3052
 
7.1%

Streaming Movies
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size378.9 KiB
No
2785 
Yes
2732 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.0712764
Min length2

Characters and Unicode

Total characters42760
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No 2785
39.5%
Yes 2732
38.8%
No internet service 1526
21.7%

Length

2025-04-20T18:56:59.769801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:57:00.032839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 4311
42.7%
yes 2732
27.1%
internet 1526
 
15.1%
service 1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 8836
20.7%
N 4311
10.1%
o 4311
10.1%
s 4258
10.0%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2732
 
6.4%
Other values (2) 3052
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8836
20.7%
N 4311
10.1%
o 4311
10.1%
s 4258
10.0%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2732
 
6.4%
Other values (2) 3052
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8836
20.7%
N 4311
10.1%
o 4311
10.1%
s 4258
10.0%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2732
 
6.4%
Other values (2) 3052
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8836
20.7%
N 4311
10.1%
o 4311
10.1%
s 4258
10.0%
3052
 
7.1%
i 3052
 
7.1%
n 3052
 
7.1%
t 3052
 
7.1%
r 3052
 
7.1%
Y 2732
 
6.4%
Other values (2) 3052
 
7.1%

Contract
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size414.9 KiB
Month-to-month
3875 
Two year
1695 
One year
1473 

Length

Max length14
Median length14
Mean length11.30115
Min length8

Characters and Unicode

Total characters79594
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-month
2nd rowMonth-to-month
3rd rowMonth-to-month
4th rowMonth-to-month
5th rowMonth-to-month

Common Values

ValueCountFrequency (%)
Month-to-month 3875
55.0%
Two year 1695
24.1%
One year 1473
 
20.9%

Length

2025-04-20T18:57:00.369918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:57:00.619794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3875
37.9%
year 3168
31.0%
two 1695
16.6%
one 1473
 
14.4%

Most occurring characters

ValueCountFrequency (%)
o 13320
16.7%
t 11625
14.6%
n 9223
11.6%
h 7750
9.7%
- 7750
9.7%
e 4641
 
5.8%
M 3875
 
4.9%
m 3875
 
4.9%
3168
 
4.0%
y 3168
 
4.0%
Other values (5) 11199
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 13320
16.7%
t 11625
14.6%
n 9223
11.6%
h 7750
9.7%
- 7750
9.7%
e 4641
 
5.8%
M 3875
 
4.9%
m 3875
 
4.9%
3168
 
4.0%
y 3168
 
4.0%
Other values (5) 11199
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 13320
16.7%
t 11625
14.6%
n 9223
11.6%
h 7750
9.7%
- 7750
9.7%
e 4641
 
5.8%
M 3875
 
4.9%
m 3875
 
4.9%
3168
 
4.0%
y 3168
 
4.0%
Other values (5) 11199
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 13320
16.7%
t 11625
14.6%
n 9223
11.6%
h 7750
9.7%
- 7750
9.7%
e 4641
 
5.8%
M 3875
 
4.9%
m 3875
 
4.9%
3168
 
4.0%
y 3168
 
4.0%
Other values (5) 11199
14.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True 4171
59.2%
False 2872
40.8%
2025-04-20T18:57:00.929340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Payment Method
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size464.9 KiB
Electronic check
2365 
Mailed check
1612 
Bank transfer (automatic)
1544 
Credit card (automatic)
1522 

Length

Max length25
Median length23
Mean length18.570212
Min length12

Characters and Unicode

Total characters130790
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMailed check
2nd rowElectronic check
3rd rowElectronic check
4th rowElectronic check
5th rowBank transfer (automatic)

Common Values

ValueCountFrequency (%)
Electronic check 2365
33.6%
Mailed check 1612
22.9%
Bank transfer (automatic) 1544
21.9%
Credit card (automatic) 1522
21.6%

Length

2025-04-20T18:57:01.255908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:57:01.522492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
check 3977
23.2%
automatic 3066
17.9%
electronic 2365
13.8%
mailed 1612
9.4%
bank 1544
 
9.0%
transfer 1544
 
9.0%
credit 1522
 
8.9%
card 1522
 
8.9%

Most occurring characters

ValueCountFrequency (%)
c 17272
13.2%
a 12354
 
9.4%
t 11563
 
8.8%
e 11020
 
8.4%
10109
 
7.7%
i 8565
 
6.5%
r 8497
 
6.5%
k 5521
 
4.2%
n 5453
 
4.2%
o 5431
 
4.2%
Other values (13) 35005
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 17272
13.2%
a 12354
 
9.4%
t 11563
 
8.8%
e 11020
 
8.4%
10109
 
7.7%
i 8565
 
6.5%
r 8497
 
6.5%
k 5521
 
4.2%
n 5453
 
4.2%
o 5431
 
4.2%
Other values (13) 35005
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 17272
13.2%
a 12354
 
9.4%
t 11563
 
8.8%
e 11020
 
8.4%
10109
 
7.7%
i 8565
 
6.5%
r 8497
 
6.5%
k 5521
 
4.2%
n 5453
 
4.2%
o 5431
 
4.2%
Other values (13) 35005
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 17272
13.2%
a 12354
 
9.4%
t 11563
 
8.8%
e 11020
 
8.4%
10109
 
7.7%
i 8565
 
6.5%
r 8497
 
6.5%
k 5521
 
4.2%
n 5453
 
4.2%
o 5431
 
4.2%
Other values (13) 35005
26.8%

Monthly Charges
Real number (ℝ)

High correlation 

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.761692
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:57:01.849033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.090047
Coefficient of variation (CV)0.46462725
Kurtosis-1.2572597
Mean64.761692
Median Absolute Deviation (MAD)24.05
Skewness-0.22052443
Sum456116.6
Variance905.41093
MonotonicityNot monotonic
2025-04-20T18:57:02.257596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 61
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
19.65 43
 
0.6%
20 43
 
0.6%
19.7 43
 
0.6%
20.15 40
 
0.6%
19.55 40
 
0.6%
19.75 39
 
0.6%
Other values (1575) 6601
93.7%
ValueCountFrequency (%)
18.25 1
 
< 0.1%
18.4 1
 
< 0.1%
18.55 1
 
< 0.1%
18.7 2
 
< 0.1%
18.75 1
 
< 0.1%
18.8 7
0.1%
18.85 5
0.1%
18.9 2
 
< 0.1%
18.95 6
0.1%
19 7
0.1%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%
117.8 1
< 0.1%
117.6 1
< 0.1%
117.5 1
< 0.1%
117.45 1
< 0.1%
117.35 1
< 0.1%
Distinct6531
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size377.6 KiB
2025-04-20T18:57:03.577895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.877467
Min length1

Characters and Unicode

Total characters41395
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6172 ?
Unique (%)87.6%

Sample

1st row108.15
2nd row151.65
3rd row820.5
4th row3046.05
5th row5036.3
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
19.65 8
 
0.1%
20.05 8
 
0.1%
19.9 8
 
0.1%
19.55 7
 
0.1%
45.3 7
 
0.1%
20.15 6
 
0.1%
20.25 6
 
0.1%
19.45 6
 
0.1%
Other values (6520) 6956
98.9%
2025-04-20T18:57:05.213070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 6768
16.3%
. 6708
16.2%
1 4234
10.2%
2 3529
8.5%
4 3466
8.4%
3 3347
8.1%
6 2964
7.2%
7 2835
6.8%
9 2661
 
6.4%
8 2640
 
6.4%
Other values (2) 2243
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 6768
16.3%
. 6708
16.2%
1 4234
10.2%
2 3529
8.5%
4 3466
8.4%
3 3347
8.1%
6 2964
7.2%
7 2835
6.8%
9 2661
 
6.4%
8 2640
 
6.4%
Other values (2) 2243
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 6768
16.3%
. 6708
16.2%
1 4234
10.2%
2 3529
8.5%
4 3466
8.4%
3 3347
8.1%
6 2964
7.2%
7 2835
6.8%
9 2661
 
6.4%
8 2640
 
6.4%
Other values (2) 2243
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 6768
16.3%
. 6708
16.2%
1 4234
10.2%
2 3529
8.5%
4 3466
8.4%
3 3347
8.1%
6 2964
7.2%
7 2835
6.8%
9 2661
 
6.4%
8 2640
 
6.4%
Other values (2) 2243
 
5.4%

Churn Label
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5174 
True
1869 
ValueCountFrequency (%)
False 5174
73.5%
True 1869
 
26.5%
2025-04-20T18:57:05.485231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Churn Value
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size344.0 KiB
0
5174 
1
1869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Length

2025-04-20T18:57:05.752952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T18:57:06.035152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Churn Score
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.699418
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:57:06.303746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile24
Q140
median61
Q375
95-th percentile94
Maximum100
Range95
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.525131
Coefficient of variation (CV)0.36670092
Kurtosis-1.0056791
Mean58.699418
Median Absolute Deviation (MAD)17
Skewness-0.089839989
Sum413420
Variance463.33125
MonotonicityNot monotonic
2025-04-20T18:57:06.674505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 151
 
2.1%
71 148
 
2.1%
77 145
 
2.1%
67 143
 
2.0%
76 141
 
2.0%
68 141
 
2.0%
70 140
 
2.0%
69 140
 
2.0%
78 138
 
2.0%
72 137
 
1.9%
Other values (75) 5619
79.8%
ValueCountFrequency (%)
5 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
20 83
1.2%
21 84
1.2%
22 82
1.2%
23 78
1.1%
24 86
1.2%
25 85
1.2%
ValueCountFrequency (%)
100 50
0.7%
99 54
0.8%
98 50
0.7%
97 64
0.9%
96 52
0.7%
95 43
0.6%
94 46
0.7%
93 47
0.7%
92 48
0.7%
91 45
0.6%

CLTV
Real number (ℝ)

Distinct3438
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.2958
Minimum2003
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.2 KiB
2025-04-20T18:57:07.047692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2296
Q13469
median4527
Q35380.5
95-th percentile6087
Maximum6500
Range4497
Interquartile range (IQR)1911.5

Descriptive statistics

Standard deviation1183.0572
Coefficient of variation (CV)0.26885855
Kurtosis-0.93403248
Mean4400.2958
Median Absolute Deviation (MAD)922
Skewness-0.3116021
Sum30991283
Variance1399624.2
MonotonicityNot monotonic
2025-04-20T18:57:07.362235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5546 8
 
0.1%
4741 7
 
0.1%
5527 7
 
0.1%
5092 7
 
0.1%
4745 7
 
0.1%
5461 7
 
0.1%
5137 7
 
0.1%
4115 7
 
0.1%
2269 7
 
0.1%
4369 7
 
0.1%
Other values (3428) 6972
99.0%
ValueCountFrequency (%)
2003 3
< 0.1%
2004 3
< 0.1%
2006 1
 
< 0.1%
2007 4
0.1%
2008 1
 
< 0.1%
2009 2
< 0.1%
2010 3
< 0.1%
2011 2
< 0.1%
2013 2
< 0.1%
2014 1
 
< 0.1%
ValueCountFrequency (%)
6500 1
 
< 0.1%
6499 2
< 0.1%
6495 1
 
< 0.1%
6494 2
< 0.1%
6492 3
< 0.1%
6491 1
 
< 0.1%
6490 1
 
< 0.1%
6489 1
 
< 0.1%
6488 1
 
< 0.1%
6487 2
< 0.1%

Churn Reason
Categorical

High correlation  Missing 

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size418.5 KiB
Attitude of support person
192 
Competitor offered higher download speeds
189 
Competitor offered more data
162 
Don't know
154 
Competitor made better offer
140 
Other values (15)
1032 

Length

Max length41
Median length31
Mean length25.194222
Min length5

Characters and Unicode

Total characters47088
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor made better offer
2nd rowMoved
3rd rowMoved
4th rowMoved
5th rowCompetitor had better devices

Common Values

ValueCountFrequency (%)
Attitude of support person 192
 
2.7%
Competitor offered higher download speeds 189
 
2.7%
Competitor offered more data 162
 
2.3%
Don't know 154
 
2.2%
Competitor made better offer 140
 
2.0%
Attitude of service provider 135
 
1.9%
Competitor had better devices 130
 
1.8%
Network reliability 103
 
1.5%
Product dissatisfaction 102
 
1.4%
Price too high 98
 
1.4%
Other values (10) 464
 
6.6%
(Missing) 5174
73.5%

Length

2025-04-20T18:57:07.694679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor 621
 
9.5%
of 542
 
8.3%
offered 351
 
5.3%
attitude 327
 
5.0%
better 270
 
4.1%
support 231
 
3.5%
service 224
 
3.4%
data 219
 
3.3%
person 192
 
2.9%
dissatisfaction 191
 
2.9%
Other values (37) 3396
51.7%

Most occurring characters

ValueCountFrequency (%)
e 5658
12.0%
o 4751
 
10.1%
4695
 
10.0%
t 4427
 
9.4%
r 3512
 
7.5%
i 3114
 
6.6%
d 2659
 
5.6%
s 2225
 
4.7%
a 1942
 
4.1%
f 1891
 
4.0%
Other values (27) 12214
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5658
12.0%
o 4751
 
10.1%
4695
 
10.0%
t 4427
 
9.4%
r 3512
 
7.5%
i 3114
 
6.6%
d 2659
 
5.6%
s 2225
 
4.7%
a 1942
 
4.1%
f 1891
 
4.0%
Other values (27) 12214
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5658
12.0%
o 4751
 
10.1%
4695
 
10.0%
t 4427
 
9.4%
r 3512
 
7.5%
i 3114
 
6.6%
d 2659
 
5.6%
s 2225
 
4.7%
a 1942
 
4.1%
f 1891
 
4.0%
Other values (27) 12214
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5658
12.0%
o 4751
 
10.1%
4695
 
10.0%
t 4427
 
9.4%
r 3512
 
7.5%
i 3114
 
6.6%
d 2659
 
5.6%
s 2225
 
4.7%
a 1942
 
4.1%
f 1891
 
4.0%
Other values (27) 12214
25.9%

Interactions

2025-04-20T18:56:36.018975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:26.776292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:28.435622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:30.002625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:31.577477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:33.124502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:34.589140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:36.219100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:27.014897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:28.673552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:30.243699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:31.837558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:33.350498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:34.797584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:36.420904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:27.269558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:28.879586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:30.470970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:32.061800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:33.572639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:35.006983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:36.631540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:27.499951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:29.105726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:30.676085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:32.285670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:33.768898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:35.203750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:36.841915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:27.734668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:29.348657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:30.904843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:32.509062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:33.992707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:35.412532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:37.030837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:27.967484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:29.571849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:31.116506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:32.720653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:34.185952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:35.608893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:37.206655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:28.197405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:29.783934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:31.332566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:32.923408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:34.384719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T18:56:35.810459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-20T18:57:07.963132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CLTVChurn LabelChurn ReasonChurn ScoreChurn ValueContractDependentsDevice ProtectionGenderInternet ServiceLatitudeLongitudeMonthly ChargesMultiple LinesOnline BackupOnline SecurityPaperless BillingPartnerPayment MethodPhone ServiceSenior CitizenStreaming MoviesStreaming TVTech SupportTenure MonthsZip Code
CLTV1.0000.1410.048-0.0790.1410.2220.0620.1020.0370.000-0.0070.0030.1080.1040.1120.1130.0150.1650.1030.0250.0000.0920.0820.0990.367-0.002
Churn Label0.1411.0001.0000.7721.0000.4100.2480.2810.0000.3220.0220.0000.2760.0360.2920.3470.1910.1500.3030.0000.1500.2300.2300.3430.3640.000
Churn Reason0.0481.0001.0000.0171.0000.0560.0000.0520.0270.0580.1030.0710.0350.0610.0600.0000.0000.0000.0160.0430.0640.0000.0440.0000.0000.101
Churn Score-0.0790.7720.0171.0000.7720.2220.2030.1470.0000.173-0.0040.0040.1310.0350.1480.1870.1470.1130.1290.0310.1240.1180.1170.183-0.238-0.003
Churn Value0.1411.0001.0000.7721.0000.4100.2480.2810.0000.3220.0220.0000.2760.0360.2920.3470.1910.1500.3030.0000.1500.2300.2300.3430.3640.000
Contract0.2220.4100.0560.2220.4101.0000.2030.2970.0000.2060.0000.0000.2530.0790.2580.3010.1770.2960.2660.0000.1430.2380.2350.3310.5150.000
Dependents0.0620.2480.0000.2030.2480.2031.0000.1780.0000.1950.0000.0240.1770.0230.1870.2060.1180.3630.1410.0000.1740.1710.1720.1940.1340.001
Device Protection0.1020.2810.0520.1470.2810.2970.1781.0000.0000.7070.0270.0210.7310.2460.7190.7170.3210.1660.2860.1720.1820.7360.7340.7260.2640.013
Gender0.0370.0000.0270.0000.0000.0000.0000.0001.0000.0000.0000.0000.0070.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.006
Internet Service0.0000.3220.0580.1730.3220.2060.1950.7070.0001.0000.0260.0160.8860.3960.7070.7240.3780.0000.3120.4520.2650.7160.7170.7230.0110.015
Latitude-0.0070.0220.103-0.0040.0220.0000.0000.0270.0000.0261.000-0.861-0.0200.0000.0420.0360.0030.0000.0190.0000.0000.0330.0240.037-0.0040.892
Longitude0.0030.0000.0710.0040.0000.0000.0240.0210.0000.016-0.8611.0000.0240.0000.0180.0190.0000.0340.0230.0000.0000.0260.0150.029-0.002-0.744
Monthly Charges0.1080.2760.0350.1310.2760.2530.1770.7310.0070.886-0.0200.0241.0000.5690.7190.7090.3600.1560.2490.6640.2340.7890.7910.7150.276-0.006
Multiple Lines0.1040.0360.0610.0350.0360.0790.0230.2460.0000.3960.0000.0000.5691.0000.2450.2290.1650.1430.1661.0000.1460.2620.2610.2300.2430.027
Online Backup0.1120.2920.0600.1480.2920.2580.1870.7190.0000.7070.0420.0180.7190.2451.0000.7180.3210.1520.2820.1720.1820.7140.7150.7200.2630.024
Online Security0.1130.3470.0000.1870.3470.3010.2060.7170.0020.7240.0360.0190.7090.2290.7181.0000.3410.1510.3040.1750.2100.7080.7080.7330.2360.028
Paperless Billing0.0150.1910.0000.1470.1910.1770.1180.3210.0000.3780.0030.0000.3600.1650.3210.3411.0000.0080.2480.0110.1560.3320.3360.3290.0000.000
Partner0.1650.1500.0000.1130.1500.2960.3630.1660.0000.0000.0000.0340.1560.1430.1520.1510.0081.0000.1610.0120.0110.1280.1360.1270.3780.018
Payment Method0.1030.3030.0160.1290.3030.2660.1410.2860.0000.3120.0190.0230.2490.1660.2820.3040.2480.1611.0000.0000.1950.2740.2730.3060.2330.000
Phone Service0.0250.0000.0430.0310.0000.0000.0000.1720.0000.4520.0000.0000.6641.0000.1720.1750.0110.0120.0001.0000.0000.1770.1800.1760.0000.029
Senior Citizen0.0000.1500.0640.1240.1500.1430.1740.1820.0000.2650.0000.0000.2340.1460.1820.2100.1560.0110.1950.0001.0000.1880.1850.2230.0220.000
Streaming Movies0.0920.2300.0000.1180.2300.2380.1710.7360.0000.7160.0330.0260.7890.2620.7140.7080.3320.1280.2740.1770.1881.0000.7710.7160.2100.024
Streaming TV0.0820.2300.0440.1170.2300.2350.1720.7340.0000.7170.0240.0150.7910.2610.7150.7080.3360.1360.2730.1800.1850.7711.0000.7160.2030.020
Tech Support0.0990.3430.0000.1830.3430.3310.1940.7260.0000.7230.0370.0290.7150.2300.7200.7330.3290.1270.3060.1760.2230.7160.7161.0000.2350.022
Tenure Months0.3670.3640.000-0.2380.3640.5150.1340.2640.0000.011-0.004-0.0020.2760.2430.2630.2360.0000.3780.2330.0000.0220.2100.2030.2351.0000.003
Zip Code-0.0020.0000.101-0.0030.0000.0000.0010.0130.0060.0150.892-0.744-0.0060.0270.0240.0280.0000.0180.0000.0290.0000.0240.0200.0220.0031.000

Missing values

2025-04-20T18:56:37.769001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-20T18:56:38.958783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
03668-QPYBK1United StatesCaliforniaLos Angeles9000333.964131, -118.27278333.964131-118.272783MaleNoNoNo2YesNoDSLYesYesNoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes1863239Competitor made better offer
19237-HQITU1United StatesCaliforniaLos Angeles9000534.059281, -118.3074234.059281-118.307420FemaleNoNoYes2YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes1672701Moved
29305-CDSKC1United StatesCaliforniaLos Angeles9000634.048013, -118.29395334.048013-118.293953FemaleNoNoYes8YesYesFiber opticNoNoYesNoYesYesMonth-to-monthYesElectronic check99.65820.5Yes1865372Moved
37892-POOKP1United StatesCaliforniaLos Angeles9001034.062125, -118.31570934.062125-118.315709FemaleNoYesYes28YesYesFiber opticNoNoYesYesYesYesMonth-to-monthYesElectronic check104.803046.05Yes1845003Moved
40280-XJGEX1United StatesCaliforniaLos Angeles9001534.039224, -118.26629334.039224-118.266293MaleNoNoYes49YesYesFiber opticNoYesYesNoYesYesMonth-to-monthYesBank transfer (automatic)103.705036.3Yes1895340Competitor had better devices
54190-MFLUW1United StatesCaliforniaLos Angeles9002034.066367, -118.30986834.066367-118.309868FemaleNoYesNo10YesNoDSLNoNoYesYesNoNoMonth-to-monthNoCredit card (automatic)55.20528.35Yes1785925Competitor offered higher download speeds
68779-QRDMV1United StatesCaliforniaLos Angeles9002234.02381, -118.15658234.023810-118.156582MaleYesNoNo1NoNo phone serviceDSLNoNoYesNoNoYesMonth-to-monthYesElectronic check39.6539.65Yes11005433Competitor offered more data
71066-JKSGK1United StatesCaliforniaLos Angeles9002434.066303, -118.43547934.066303-118.435479MaleNoNoNo1YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthNoMailed check20.1520.15Yes1924832Competitor made better offer
86467-CHFZW1United StatesCaliforniaLos Angeles9002834.099869, -118.32684334.099869-118.326843MaleNoYesYes47YesYesFiber opticNoYesNoNoYesYesMonth-to-monthYesElectronic check99.354749.15Yes1775789Competitor had better devices
98665-UTDHZ1United StatesCaliforniaLos Angeles9002934.089953, -118.29482434.089953-118.294824MaleNoYesNo1NoNo phone serviceDSLNoYesNoNoNoNoMonth-to-monthNoElectronic check30.2030.2Yes1972915Competitor had better devices
CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
70330871-OPBXW1United StatesCaliforniaTwentynine Palms9227734.17211, -115.76977334.172110-115.769773FemaleNoNoNo2YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthYesMailed check20.0539.25No0805191NaN
70343605-JISKB1United StatesCaliforniaTwentynine Palms9227834.457829, -116.13958934.457829-116.139589MaleYesYesNo55YesYesDSLYesYesNoNoNoNoOne yearNoCredit card (automatic)60.003316.1No0714212NaN
70359767-FFLEM1United StatesCaliforniaWestmorland9228133.03679, -115.6050333.036790-115.605030MaleNoNoNo38YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesCredit card (automatic)69.502625.25No0354591NaN
70368456-QDAVC1United StatesCaliforniaWinterhaven9228332.852947, -114.85078432.852947-114.850784MaleNoNoNo19YesNoFiber opticNoNoNoNoYesNoMonth-to-monthYesBank transfer (automatic)78.701495.1No0202464NaN
70377750-EYXWZ1United StatesCaliforniaYucca Valley9228434.159534, -116.42598434.159534-116.425984FemaleNoNoNo12NoNo phone serviceDSLNoYesYesYesYesYesOne yearNoElectronic check60.65743.3No0243740NaN
70382569-WGERO1United StatesCaliforniaLanders9228534.341737, -116.53941634.341737-116.539416FemaleNoNoNo72YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceTwo yearYesBank transfer (automatic)21.151419.4No0455306NaN
70396840-RESVB1United StatesCaliforniaAdelanto9230134.667815, -117.53618334.667815-117.536183MaleNoYesYes24YesYesDSLYesNoYesYesYesYesOne yearYesMailed check84.801990.5No0592140NaN
70402234-XADUH1United StatesCaliforniaAmboy9230434.559882, -115.63716434.559882-115.637164FemaleNoYesYes72YesYesFiber opticNoYesYesNoYesYesOne yearYesCredit card (automatic)103.207362.9No0715560NaN
70414801-JZAZL1United StatesCaliforniaAngelus Oaks9230534.1678, -116.8643334.167800-116.864330FemaleNoYesYes11NoNo phone serviceDSLYesNoNoNoNoNoMonth-to-monthYesElectronic check29.60346.45No0592793NaN
70423186-AJIEK1United StatesCaliforniaApple Valley9230834.424926, -117.18450334.424926-117.184503MaleNoNoNo66YesNoFiber opticYesNoYesYesYesYesTwo yearYesBank transfer (automatic)105.656844.5No0385097NaN